Results 231 to 240 of about 118,175 (305)

Harnessing Non‐Covalent Protein–Protein Interaction Domains for Production of Biocatalytic Materials Systems

Advanced Functional Materials, EarlyView.
Non‐covalent protein–protein interactions mediated by SH3, PDZ, or GBD domains enable the self‐assembly of stable and biocatalytically active hydrogel materials. These soft materials can be processed into monodisperse foams that, once dried, exhibit enhanced mechanical stability and activity and are easily integrated into microstructured flow ...
Julian S. Hertel, Maria Alessandra Martini, Marius Stoeckle, Annika J. Weber, Kersten S. Rabe, Christof M. Niemeyer   +5 more
wiley   +1 more source

Multicolor Optoelectronic Synapse Enabled by Photon‐Modulated Remote Doping in Solution‐Processed Van Der Waals Heterostructures

Advanced Functional Materials, EarlyView.
Multicolor optoelectronic synapses are realized by vertically integrating solution‐processed MoS2 thin‐film and SWCNT. The electronically disconnected but interactive MoS2 enables photon‐modulated remote doping, producing a bi‐directional photoresponse.
Jihyun Kim, Hyunbin Kim, Gang Jin Cheon, Dohyun Lee, In Soo Kim, Uiyeon Won, Jongseok Lee, Sang Min Won, Joohoon Kang   +8 more
wiley   +1 more source

LOSARI: A novel R-based statistical software to facilitate students' self-regulated learning in statistics courses. [PDF]

MethodsX
Bakri R, Del Val EB, Bado B, Ahmar AS.
europepmc   +1 more source

Environmental and Socio-Economic Impacts Analysis of the Introduction of Renewable Energy Technologies Using Input : Output Tables

, 2016
峻一 稗貫
openalex   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Ecological Study on Input-Output Table

Input-Output Analysis, 1991
openaire   +2 more sources

Complex Cryptographic and User‐Centric Physically Unclonable Functions Enabled by Strain‐Sensitive Nanocrystals via Selective Ligand Exchange

Advanced Functional Materials, EarlyView.
This study investigates electromechanical PUFs that improve on traditional electric PUFs. The electron transport materials are coated randomly through selective ligand exchange. It produces multiple keys and a key with motion dependent on percolation and strain, and approaches almost ideal inter‐ and intra‐hamming distances.
Seungshin Lim, Hanseok Seo, Taesung Park, Byung Ku Jung, Junhyeok Park, Young Kyun Choi, Dae Yang Oh, Soong Ju Oh   +7 more
wiley   +1 more source

Prediction of crack repair percentage in self-healing concrete using machine learning. [PDF]

Sci Rep
Khosravi H, Kiani P, Bahram M, Lezgy-Nazargah M.   +3 more
europepmc   +1 more source

Multi-input–output table.

Xiongquan Ou (21722284), Ming Ma (43147), Wei Wang (17594)   +2 more
openalex   +1 more source

Rapid Sintering of Porous Organic Polymer Powders Into Mechanically Strong Monoliths for Efficient CO2 Capture

Advanced Functional Materials, EarlyView.
Spark plasma sintering enables rapid processing of porous organic polymer (POP) from fine powders to a freestanding and mechanically robust monolith. The sintering process retains the CO2 adsorption capacity and CO2/N2 selectivity, making the monolith more suitable as a solid sorbent for practical carbon capture applications.
Duan Li, Tianhui Shi, Sirui Ran, Lei Zeng, Chao Xu   +4 more
wiley   +1 more source